Package 'baker'

Description

add likelihood for a BrS measurement slice among cases (conditional dependence)

Usage

add_meas_BrS_case_Nest_Slice(s, Mobs, cause_list, ppd = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add likelihood for a BrS measurement slice among cases (conditional dependence)

Usage

add_meas_BrS_case_Nest_Slice_jags(s, Mobs, cause_list, ppd = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

regression model with no nested subclasses; discrete predictors

Usage

add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(
  s,
  Mobs,
  prior,
  cause_list,
  ppd = NULL
)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

regression model with no nested subclasses

Usage

add_meas_BrS_case_NoNest_reg_Slice_jags(s, Mobs, prior, cause_list, ppd = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add a likelihood component for a BrS measurement slice among cases (conditional independence)

Usage

add_meas_BrS_case_NoNest_Slice(s, Mobs, cause_list, ppd = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add a likelihood component for a BrS measurement slice among cases (conditional independence)

Usage

add_meas_BrS_case_NoNest_Slice_jags(s, Mobs, prior, cause_list, ppd = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add likelihood for a BrS measurement slice among controls (conditional independence)

Usage

add_meas_BrS_ctrl_Nest_Slice(s, Mobs, cause_list, ppd = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

regression model without nested subclasses; discrete

Usage

add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(
  s,
  Mobs,
  cause_list,
  ppd = NULL
)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

regression model without nested subclasses

Usage

add_meas_BrS_ctrl_NoNest_reg_Slice_jags(s, Mobs, cause_list, ppd = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add a likelihood component for a BrS measurement slice among controls (conditional independence)

Usage

add_meas_BrS_ctrl_NoNest_Slice(s, Mobs, cause_list, ppd = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

regression model with nested subclasses; called by insert_bugfile_chunk_reg_nest_meas

Usage

add_meas_BrS_param_Nest_reg_Slice_jags(
  s,
  Mobs,
  prior,
  cause_list,
  FPR_formula = NULL
)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add parameters for a BrS measurement slice among cases and controls (conditional dependence)

Usage

add_meas_BrS_param_Nest_Slice(s, Mobs, cause_list)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add parameters for a BrS measurement slice among cases and controls (conditional dependence)

Usage

add_meas_BrS_param_Nest_Slice_jags(s, Mobs, cause_list)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

regression model with no nested subclasses; discrete

Usage

add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(
  s,
  Mobs,
  prior,
  cause_list
)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

regression model with no nested subclasses

Usage

add_meas_BrS_param_NoNest_reg_Slice_jags(
  s,
  Mobs,
  prior,
  cause_list,
  FPR_formula
)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add parameters for a BrS measurement slice among cases and controls (conditional independence)

Usage

add_meas_BrS_param_NoNest_Slice(s, Mobs, cause_list)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add parameters for a BrS measurement slice among cases and controls (conditional independence)

Usage

add_meas_BrS_param_NoNest_Slice_jags(s, Mobs, prior, cause_list)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case(), add_meas_SS_param()

Description

add subclass indicators for a BrS measurement slice among cases and controls (conditional independence)

Usage

add_meas_BrS_subclass_Nest_Slice(s, Mobs, cause_list, ppd = NULL, reg = NULL)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_SS_case(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_SS_case(), add_meas_SS_param()

Description

add likelihood for a SS measurement slice among cases (conditional independence)

Usage

add_meas_SS_case(nslice, Mobs, prior, cause_list)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_param()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_param()

Description

add parameters for a SS measurement slice among cases (conditional independence)

Usage

add_meas_SS_param(nslice, Mobs, prior, cause_list)

Arguments

Value

a list of two elements: the first is plug, the .bug code; the second is parameters that stores model parameters introduced by this plugged measurement slice

See Also

Other likelihood specification functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case()

Other plug-and-play functions: add_meas_BrS_case_Nest_Slice_jags(), add_meas_BrS_case_Nest_Slice(), add_meas_BrS_case_NoNest_Slice_jags(), add_meas_BrS_case_NoNest_Slice(), add_meas_BrS_case_NoNest_reg_Slice_jags(), add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_ctrl_Nest_Slice(), add_meas_BrS_ctrl_NoNest_Slice(), add_meas_BrS_ctrl_NoNest_reg_Slice_jags(), add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_param_Nest_Slice_jags(), add_meas_BrS_param_Nest_Slice(), add_meas_BrS_param_Nest_reg_Slice_jags(), add_meas_BrS_param_NoNest_Slice_jags(), add_meas_BrS_param_NoNest_Slice(), add_meas_BrS_param_NoNest_reg_Slice_jags(), add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(), add_meas_BrS_subclass_Nest_Slice(), add_meas_SS_case()

Description

convert one column data frame to a vector

Usage

as.matrix_or_vec(x)

Arguments

Details

JAGS cannot accept a data frame with one column; This function converts it to a vector, which JAGS will allow.

Value

a vector

Description

assign_model translates options specified by a user (e.g., in model_options) into information that can be understood by baker.

Usage

assign_model(model_options, data_nplcm, silent = TRUE)

Arguments

Details

assign_model will be modified to check if data are conformable to specified model.

Value

A list of model specifications:

• num_slice A vector counting the No. of measurement slices for each level of measurement quality (e.g., MBS, MSS, MGS representing Bronze-Standard Measurements - case-control, Silver-Standard Measurements and Gold-Standard Measurements - case-only);

• nested Local dependence specification for modeling bronze-standard data. TRUE for nested models (conditional dependence given disease class); FALSE for non-nested models (conditional independence given disease class). One for each BrS slice.

• regression

  • do_reg_Eti TRUE for doing etiology regression. It means let the etiology fractions vary with explanatory variables. FALSE otherwise;

  • do_reg_FPR A vector whose names represent the slices of bronze-standard data. For each slice of BrS measurements, TRUE does false positive rate regression. It means the false positive rates, estimatable from controls, can vary with covariates; FALSE otherwise.

  • is_discrete_predictor A list of names "Eti", and the names for every slice of bronze-standard data. TRUE if all predictors are discrete; FALSE otherwise.

Examples

cause_list <- c(LETTERS[1:6]) 
J.BrS <- 6
model_options_no_reg <- list(
likelihood   = list(
  cause_list = cause_list,
  k_subclass = 2,
  Eti_formula = ~-1, 
  # no covariate for the etiology regression
  FPR_formula = list(
    MBS1 =   ~-1)    
    # no covariate for the subclass weight regression
),
use_measurements = c("BrS"), 
# use bronze-standard data only for model estimation.
prior= list(
  Eti_prior = overall_uniform(1,cause_list), 
  # Dirichlet(1,...,1) prior for the etiology.
  TPR_prior  = list(BrS = list(
    info  = "informative", # informative prior for TPRs
    input = "match_range", 
    # specify the informative prior for TPRs by specifying a plausible range.
    val = list(MBS1 = list(up =  list(rep(0.99,J.BrS)), 
    # upper ranges: matched to 97.5% quantile of a Beta prior
                           low = list(rep(0.55,J.BrS))))
                           # lower ranges: matched to 2.5% quantile of a Beta prior
  )
  )
)
)     
data("data_nplcm_noreg")

assign_model(model_options_no_reg,data_nplcm_noreg)

Description

baker is designed for disease etiology studies from case-control data with multiple sources of measurements with potential errors. If you are interested in estimating the population etiology pie (a vector of fractions that sum to one), and the probability of each cause for a particular individual case, try baker.

Details

baker implements hierarchical Bayesian models to infer disease etiology for multivariate binary data. We created baker to catalyze effective communications between analysts and practicing clinicians that are vital to the success of etiology studies. The baker package offers modules to

• Import and tidy the PERCH data (the study that motivates the creation of this package),

• Transform, explore the data,

• Specify, automatically generate the model files, and fit the models (npLCM),

• Store and visualize posterior summaries for communicating scientific findings, and

• Check and compare the fitted models.

baker has implemented models for dependent measurements given disease status, regression analyses of etiology, multiple imperfect measurements, different priors for true positive rates among cases with differential measurement characteristics, and multiple-pathogen etiology. Scientists in Pneumonia Etiology Research for Child Health (PERCH) study usually refer to the etiology distribution as "population etiology pie" and "individual etiology pie" for their compositional nature, hence the name of the package (baking the pie).

Value

No returned value; documentation purpose only.

baker functions

nplcm()

See Also

• https://github.com/zhenkewu/baker for the source code and system/software requirements to use baker for your data.

Description

beta_parms_from_quantiles produces prior Beta parameters for the true positive rates (TPR)

Usage

beta_parms_from_quantiles(
  q,
  p = c(0.025, 0.975),
  precision = 0.001,
  derivative.epsilon = 0.001,
  start.with.normal.approx = TRUE,
  start = c(1, 1),
  plot = FALSE
)

Arguments

Value

A list containing the selected Beta parameters a, and b. Other elements of the list include some details about the computations involved in finding a and b.

References

http://www.medicine.mcgill.ca/epidemiology/Joseph/PBelisle/BetaParmsFromQuantiles.html

Examples

beta_parms_from_quantiles(c(0.5,0.99))

Description

Plot beta density

Usage

beta_plot(a, b)

Arguments

Value

None

Examples

beta_plot(2,2)

Description

Useful when try to list all the binary patterns. One can group the binary sequences according to their equivalent decimal values.

Usage

bin2dec(binary_vector)

Arguments

Value

a decimal number

Examples

bin2dec(c(1,0,1))

Description

check existence and create folder if non-existent

Usage

check_dir_create(path)

Arguments

Value

the same returned values for dir.create()

Examples

check_dir_create(tempdir())

Description

In the Actual PERCH data set, a study site may have multiple subsites. clean_combine_subsites combines all the study subjects from the same site.

Usage

clean_combine_subsites(raw_meas_dir, subsites_list, newsites_vec)

Arguments

Value

A data frame with combined sites

Description

clean_perch_data transforms a raw data table (row for subjects, column for variables - named as {pathogen name}_{specimen}{test} for lab tests or other covariates) into a list. It is designed for PERCH data format.

Usage

clean_perch_data(clean_options)

Arguments

Value

A List: list(Mobs,Y,X)

• Mobs A list of bronze- (MBS), silver- (MSS), and gold-standard (MGS, if available) measurements. See the formats of these measurements in extract_data_raw().

• Y 1 for case; 0 for control;

• X Data frame of covariates for cases and controls. The covariate names are specified in X_extra;

This function does not re-order pathogens that only have silver-standard data.

See Also

make_meas_object for wrapping information about a particular type of measurement; extract_data_raw for reading raw data table and organizing them into data_nplcm format. Also see clean_combine_subsites for combining subsites and parse_date_time for parsing date.

Description

combine multiple data_nplcm (useful when simulating data from regression models)

Usage

combine_data_nplcm(data_nplcm_list)

Arguments

Value

a list with each element resulting from row binding of each corresponding element in the input data_nplcm_list.

See Also

Other data operation functions: merge_lists(), subset_data_nplcm_by_index()

Examples

N=100
Y = rep(c(1,0),times=50) # simulate two cases and two controls.
out_list <- vector("list",length=N)
J = 3                          # number of causes
cause_list = c(LETTERS[1:J])   # cause list
K = 2                          # number of subclasses
lambda = c(.8,.2)                # subclass weights for control group
eta = c(.9,.1)                   # subclass weights for case group

for (i in 1:N){
  #setup parameters for the present individual:
  set_parameter <- list(
    cause_list      = cause_list,
    etiology        = c(0.5,0.2,0.3), # only meaningful for cases
    pathogen_BrS    = LETTERS[1:J],
    pathogen_SS     = LETTERS[1:2],
    meas_nm         = list(MBS = c("MBS1"),MSS=c("MSS1")),
    Lambda          = lambda,         # for BrS
    Eta             = t(replicate(J,eta)),  # case subclass weight for BrS
    PsiBS           = cbind(c(0.15,0.3,0.35),
                            c(0.25,0.2,0.15)), # FPR
    PsiSS           = cbind(rep(0,J),rep(0,J)),
    ThetaBS         = cbind(c(0.95,0.9,0.85),    # TPR
                            c(0.95,0.9,0.85)),
    ThetaSS         = cbind(c(0.25,0.10),
                            c(0.25,0.10)),
    Nd      =     1,
    Nu      =     1
  )
  simu_out   <- simulate_nplcm(set_parameter)
  out <- simu_out$data_nplcm
  out_list[[i]] <- out
}

# extract cases and controls and combine all the data into one:
data_nplcm_list <- lapply(1:N, function(s) subset_data_nplcm_by_index(out_list[[s]],2-Y[s]))
data_nplcm_unordered      <- combine_data_nplcm(data_nplcm_list)

Description

This only works for single-agent causes

Usage

compute_logOR_single_cause(set_parameter)

Arguments

Value

a matrix of log odds ratio. See the example for a figure showing pairwise odds ratios for cases (upper right, solid lines) and controls (lower left, broken lines) as the first subclass weight increases from 0 to 1. Pairwise independence is represented by the dotted horizontal lines for reference.

Examples

K.true  <- 2   # no. of latent subclasses in actual simulation. 
               # If eta = c(1,0), effectively, it is K.true=1
J       <- 5   # no. of pathogens.
N       <- 500 # no. of cases/controls.

col_seq_cause <-  c("#DB9D85","#A2B367","#47BEA2",
"#70B3DA","#CD99D8")#colorspace::rainbow_hcl(5, start = 30, end = 300)

subclass_mix_seq <- seq(0,1,by=0.05)
res      <- array(NA,c(J,J,length(subclass_mix_seq)))
res_cond <- array(NA,c(J,J,length(subclass_mix_seq),J))

it <- layout(matrix(1:J^2,nrow=J,ncol=J,byrow=TRUE),
            heights = rep(3,J),
            widths  = rep(3,J)) 

oldpar <- par(oma=c(8,10,8,3));  

pch_seq_cause <- LETTERS[1:J]
lty_seq_cause <- 1+(1:J)
pch_pos_seq   <- c(0.01); gap = 0.15
adj_seq <- c(0.15,0.5,0.85) # for roman numerals:
cex1       <- 2
cex_label1 <- 1
cex2       <- 2
cex_label2 <- 2
cex_margin_marks <- 2

for (scn in c(1,2,3)){
 for (iter in seq_along(subclass_mix_seq)){
   curr_mix <- subclass_mix_seq[iter]
   lambda <- c(curr_mix,1-curr_mix)
   eta    <- c(curr_mix,1-curr_mix) 
   # if it is c(1,0),then it is conditional independence model, and
   # only the first column of parameters in PsiBS, ThetaBS matter!
   
   seed_start <- 20150923
   
   # set fixed simulation sequence:
   set.seed(seed_start)
   
   if (scn == 3){
     ThetaBS_withNA <- cbind(c(0.95,0.9,0.1,0.5,0.5),
                             c(0.95,0.1,0.9,0.5,0.5))
     PsiBS_withNA   <- cbind(c(0.4,0.4,0.05,0.2,0.2),
                             c(0.05,0.05,0.4,0.05,0.05))
   }
   
   if (scn == 2){
     ThetaBS_withNA <- cbind(c(0.95,0.5,0.5,0.5,0.5),
                             c(0.95,0.5,0.5,0.5,0.5))
     PsiBS_withNA   <- cbind(c(0.4,0.4,0.05,0.2,0.2),
                             c(0.05,0.05,0.4,0.05,0.05))
   }
   
   if (scn == 1){
     ThetaBS_withNA <- cbind(c(0.95,0.5,0.5,0.5,0.5),
                             c(0.95,0.5,0.5,0.5,0.5))
     PsiBS_withNA   <- cbind(c(0.3,0.3,0.15,0.2,0.2),
                             c(0.15,0.15,0.3,0.05,0.05))
   }
   
   # the following paramter names are set using names in the 'baker' package:
   set_parameter0 <- list(
     cause_list      = c(LETTERS[1:J]),
     etiology        = c(0.5,0.2,0.15,0.1,0.05), #same length as cause_list
     #etiology        = rep(0.2,J), #same length as cause_list
     pathogen_BrS    = LETTERS[1:J],
     meas_nm         = list(MBS = c("MBS1")),
     Lambda          = lambda,              #ctrl mix
     Eta             = t(replicate(J,eta)), #case mix, row number equal to Jcause.
     PsiBS           = PsiBS_withNA,
     ThetaBS         = ThetaBS_withNA,
     Nu      =     N, # control size.
     Nd      =     N  # case size.
   )
   
   res[,,iter] <- round(compute_logOR_single_cause(set_parameter0),2)
   
   for (pick in 1:J){
     set_parameter <- set_parameter0
     set_parameter$ThetaBS <- set_parameter0$PsiBS
     set_parameter$ThetaBS[pick,] <- set_parameter0$ThetaBS[pick,]
     set_parameter$etiology <- rep(0,J); set_parameter$etiology[pick] <- 1
     res_cond[,,iter,pick] <- round(compute_logOR_single_cause(set_parameter),2)
   }
 }
 
 ind <- sapply(c(0,0.5,1),function(x) which(subclass_mix_seq==x))
 logOR_lim <- c(-2.15,2.15)
 col_seq <- c("dodgerblue2","orange")
 logOR_seq <- log(c(0.25,0.5,1,2,4))
 pick_one <- 3

 print(paste0("==Shading pairs of ",pch_seq_cause[pick_one]," and others.==="))
 for (j in 1:J){
   for (l in 1:J){
     
     par(mar=c(0,0,0,0)); 
     if (j==J){
       par(mar=c(0,0,0,0))
     }
     if (l%%J==0){
       par(mar=c(0,0,0,1)) 
     }
     if (l%%J==1){
       par(mar=c(0,1,0,0))
     }
     if (!(j==l)){
       plot(res[j,l,],type="l",xlab="",ylab="",
            ylim=logOR_lim, lwd=5,
            xaxt="n",
            yaxt="n",
            col=col_seq[1+(l>j)],
            #lty=c(2,1)[1+(l>j)],
            lty=1,
            bty="n"
       )
       box(col="lightgray")
       abline(h=0,col="lightgray",lwd=3,lty=3)
       
       if (j<l){
         matplot(res_cond[j,l,,],type="l",add=TRUE,pch=LETTERS[1:J],lwd=2,lty=2,
                 col=col_seq_cause)
       }
       lab_ord <- c(j,l); if (j>l){lab_ord <- rev(lab_ord)}
       mtext(paste0("(",set_parameter$pathogen_BrS[lab_ord[1]],",", 
                    set_parameter$pathogen_BrS[lab_ord[2]],")"), 
             side=3, adj=0.1,line=-2)
       
       if (l%%J==1){
         axis(2,at = logOR_seq, 
              labels = round(exp(logOR_seq),1),
              las=2,cex.axis=cex1)
       }
       
       if (l%%J==0){
         axis(4,at = logOR_seq, 
              labels = round(exp(logOR_seq),1),
              las=2,cex.axis=cex1)
       }
       
       if (j==J){
         axis(1,at=seq_along(subclass_mix_seq)[ind],
         labels=rep("",length(ind)),cex.axis = cex1,las=1)
         axis(1,at=seq_along(subclass_mix_seq)[ind]+c(1,rep(0,length(ind)-2),-1),
         labels=subclass_mix_seq[ind],cex.axis = cex1,las=1,tick=FALSE)
       }
       if (j==1){
         axis(3,at=seq_along(subclass_mix_seq)[ind],
         labels=rep("",length(ind)),cex.axis = cex1,las=1)
         axis(3,at=seq_along(subclass_mix_seq)[ind]+c(1,rep(0,length(ind)-2),-1),
         labels=subclass_mix_seq[ind],cex.axis = cex1,las=1,tick=FALSE)
       }
       if (j==5 & l==1){
         mtext(expression(atop("Odds Ratio","(log-scale)")), side = 2, line = 4, 
               cex=cex_label1, las=2)
       }
       if (j==5){
         mtext(expression(lambda[o]),side=1,line=4,cex=cex_label1)
       }
       
       if ((j<l) && (l==pick_one | j==pick_one )){
         # add shading cells for oen picked pathogen among cases:
         color <- rgb(190, 190, 190, alpha=80, maxColorValue=255)
         rect(par("usr")[1], par("usr")[3], par("usr")[2], 
              par("usr")[4], density = 100, col = color)
         
         matplot(res_cond[j,l,,],type="l",add=TRUE,lwd=2,col=col_seq_cause,lty=lty_seq_cause)
         for (ell in 1:J){
           where_add_letter <- quantile(seq_along(subclass_mix_seq),pch_pos_seq+gap*ell)
           points(where_add_letter, res_cond[j,l,where_add_letter,ell], pch=pch_seq_cause[ell])
         }
         mtext(paste0("(",set_parameter$pathogen_BrS[lab_ord[1]],",", 
                      set_parameter$pathogen_BrS[lab_ord[2]],")"), 
               side=3, adj=0.1,line=-2)
       }
       
     }else{
       
       plot(1, type="n", axes=FALSE, xlab="", ylab="", bty="n",
            xlim=c(0,1),ylim=c(0,1))
       
       
       if (j==3){
         text(labels=expression(CASES%up%""),x=.7,
              y=0.55,srt=-49,col=col_seq[2],cex=1.8,adj=0.5,font=4)
         text(labels=expression(CONTROLS%down%""),x=.42,
              y=0.38,srt=-49,col=col_seq[1],cex=1.8,adj=0.5,font=4)
       }
       if (j!=1 & j!=J){
         dg <- par("usr") 
         segments(dg[1],dg[4],dg[2],dg[3], col='lightgray',lwd=3)
       }
       if (j==J){
         legend("top",LETTERS[1:J],lty=2,col=col_seq_cause,cex = 1.5,lwd=2,
                bty="n",horiz=FALSE)
       }
     }
   }
 }
}
par(oldpar)

Description

The array version of this function (compute_marg_PR_nested_reg_array) is used in plot_etiology_regression

Usage

compute_marg_PR_nested_reg(ThetaBS, PsiBS, pEti_mat, subwt_mat, case, template)

Arguments

Value

a matrix of values between 0 and 1 (need not to have row sums of ones); of dimension (number of subjects, dimension of the bronze-standard measurement slice).

Description

This is an array-version of compute_marg_PR_nested_reg. This is used in plot_etiology_regression

Usage

compute_marg_PR_nested_reg_array(
  ThetaBS_array,
  PsiBS_array,
  pEti_mat_array,
  subwt_mat_array,
  case,
  template
)

Arguments

Value

An array of: a matrix of values between 0 and 1 (need not to have row sums of ones); of dimension (number of subjects, dimension of the bronze-standard measurement slice).

Description

create_bugs_regressor_Eti creates linear product of coefficients and a row of design matrix used in regression

Usage

create_bugs_regressor_Eti(
  n,
  dm_nm = "dm_Eti",
  b_nm = "betaEti",
  ind_nm = "j",
  sub_ind_nm = "k"
)

Arguments

Value

a character string with linear product form

Description

create_bugs_regressor_FPR creates linear product of coefficients and a row of design matrix used in regression

Usage

create_bugs_regressor_FPR(
  n,
  dm_nm = "dm_FPR",
  b_nm = "b",
  ind_nm = "j",
  sub_ind_nm = "k"
)

Arguments

Value

a character string with linear product form

Simulated dataset that is structured in the format necessary for an nplcm() without regression

Description

Data set for illustrating regression functionalities

Usage

data("data_nplcm_noreg")

Format

A list containing three items

Mobs

BrS level measurements: N = 600 (half cases and half controls); one slice of BrS measurements (6 dimensional, A-F); one slice of SS measurements (2 dimensional, A and B)

Y

case-control status

Value

No returned value; just loading data into the working space.

Simulated dataset that is structured in the format necessary for an nplcm() with regression

Description

Data set for illustrating regression functionalities

Usage

data("data_nplcm_reg_nest")

Format

A list containing three items

Mobs

BrS level measurements: N = 1,200 (half cases and half controls); one slice of BrS measurements (6 dimensional, A-F); one slice of SS measurements (2 dimensional, A and B)

Y

case-control status

X

matrix of covariates (N by 4); columns: SITE (1 and 2, each with 600 subjects), DATE (index from 1:300), std_date (standardized DATE), ENRLDATE (actual dates)

Value

No returned value; just loading data into the working space.

Description

delete_start_with is used for clean the column names in raw data. For example, R adds "X" at the start of variable names. This function deletes "X_"s from the column names. This can happen if the raw data have column names such as "⁠_CASE_ABX⁠". Check clean_perch_data() for its actual usage.

Usage

delete_start_with(s, vec)

Arguments

Value

string(s) with deleted patterns from the start.

Examples

delete_start_with("X_",c("X_hello"))
delete_start_with("X_",c("X_hello","hello2"))
delete_start_with("X_",c("X_hello","hello2","X_hello3"))

Description

dm_Rdate_Eti creates design matrices for etiology regressions.

Usage

dm_Rdate_Eti(Rdate, Y, num_knots_Eti, basis_Eti = "ncs")

Arguments

Details

It is used in model_options$likeihood$Eti_formula. For example, one can specify it as:

~ AGECAT+HIV+dm_Rdate_Eti(ENRLDATE,Y,5)

to call an etiology regression with intercept, main effects for 'AGECAT' and 'HIV', and natural cubic spline bases for 'ENRLDATE' using 5 knots defined as 5 equal-probability-spaced sample quantiles.

Value

Design matrix for etiology regression:

• Z_Eti transformed design matrix for etiology regression

See Also

nplcm()

Description

dm_Rdate_FPR creates design matrices for false positive rate regressions; can also be used to standardize dates.

Usage

dm_Rdate_FPR(Rdate, Y, effect = "fixed", num_knots_FPR = NULL)

Arguments

Value

Design matrix for FPR regression:

• Z_FPR_ctrl transformed design matrix for FPR regression for controls

• Z_FPR_case transformed design matrix for borrowing FPR regression from controls to cases. It is obtained using control-standardization, and square-root the following matrix ($\Omega$]) with ($j_1$,$j_2$) element being

  $\Omega_{j_1j_2}=\|knots_{j_1}-knots_{j_2}\|^3$

  .

See Also

nplcm()

Description

expit function

Usage

expit(x)

Arguments

Value

a Probability between 0 and 1

Examples

expit(-0.1)

Description

Import Raw PERCH Data

extract_data_raw imports and converts the raw data to analyzable format

Usage

extract_data_raw(
  dat_prepared,
  strat_nm,
  strat_val,
  meas_object,
  extra_covariates = NULL
)

Arguments

Value

A list of data.

Mobs

MBS

A list of Bronze-Standard (BrS) measurements. The names of the list take the form of specimen_test. Each element of the list is a data frame. The rows of the data frame are for subjects; the columns are for measured pathogens.

MSS

A list of Silver-Standard (SS) measurements. The formats are the same as MBS above.

MGS

A list of Gold-Standard (GS) measurements. It equals NULL if no GS data exist.

X

A data frame with columns specified by extra_covariates.

See Also

clean_perch_data()

Other raw data importing functions: read_meas_object()

Description

Obtain coverage status from a result folder

Usage

get_coverage(DIR_NPLCM, truth)

Arguments

Value

A logic vector of length as truth. 1 for covered; 0 for not.

Description

Obtain direct bias that measure the discrepancy of a posterior distribution of pie and a true pie.

Usage

get_direct_bias(DIR_list, truth = NULL, silent = FALSE)

Arguments

Value

a list of length two. diff is the direct differences; prb is the percent relative bias.

Description

get fitted mean for nested model with subclass mixing weights that are the same among cases

Usage

get_fitted_mean_nested(
  slice,
  res_nplcm,
  model_options,
  data_nplcm,
  clean_options
)

Arguments

Value

a matrix of no. of rows equal to retained MCMC samples, no. of columns equal to the no. of measurement dimensions within a slice.

Description

get model fitted mean for conditional independence model

Usage

get_fitted_mean_no_nested(
  slice,
  res_nplcm,
  model_options,
  data_nplcm,
  clean_options
)

Arguments

Value

a list with model fitted means

Description

get individual data

Usage

get_individual_data(i, data_nplcm)

Arguments

Value

a list of the same structure as data_nplcm; just with one row of values

Examples

data(data_nplcm_noreg)
get_individual_data(2,data_nplcm_noreg)

Description

must set individual.pred = TRUE in MCMC options (see the example of this function)

Usage

get_individual_prediction(x)

Arguments

Value

a matrix of individual predictions; rows for cases, columns for causes specified in model_options$likelihood$cause_list; See nplcm()

Examples

data(data_nplcm_noreg)
cause_list <- LETTERS[1:6]
J.BrS      <- 6
model_options_no_reg <- list(
  likelihood   = list(
    cause_list = cause_list,
    k_subclass = 2,
    Eti_formula = ~-1, # no covariate for the etiology regression
    FPR_formula = list(
      MBS1 =   ~-1)    # no covariate for the subclass weight regression
  ),
  use_measurements = c("BrS"), 
  # use bronze-standard data only for model estimation.
  prior= list(
    Eti_prior = overall_uniform(1,cause_list), 
    # Dirichlet(1,...,1) prior for the etiology.
    TPR_prior  = list(BrS = list(
      info  = "informative", # informative prior for TPRs
      input = "match_range", 
      # specify the informative prior for TPRs by specifying a plausible range.
      val = list(MBS1 = list(up =  list(rep(0.99,J.BrS)), 
                             # upper ranges: matched to 97.5% quantile of a Beta prior
                             low = list(rep(0.55,J.BrS))))
      # lower ranges: matched to 2.5% quantile of a Beta prior
    )
    )
  )
)     


set.seed(1)
# include stratification information in file name:
thedir    <- paste0(tempdir(),"_no_reg")

# create folders to store the model results 
dir.create(thedir, showWarnings = FALSE)
result_folder_no_reg <- file.path(thedir,paste("results",collapse="_"))
thedir <- result_folder_no_reg
dir.create(thedir, showWarnings = FALSE)

# options for MCMC chains:
mcmc_options_no_reg <- list(
  debugstatus = TRUE,
  n.chains = 1,
  n.itermcmc = as.integer(200), 
  n.burnin = as.integer(100), 
  n.thin = 1,
  individual.pred = TRUE, # <- must set to TRUE! 
  ppd = FALSE,
  result.folder = thedir,
  bugsmodel.dir = thedir
)

BrS_object_1 <- make_meas_object(patho = LETTERS[1:6], 
                                 specimen = "MBS", test = "1", 
                                 quality = "BrS", cause_list = cause_list)
clean_options <- list(BrS_objects = make_list(BrS_object_1))
# place the nplcm data and cleaning options into the results folder
dput(data_nplcm_noreg,file.path(thedir,"data_nplcm.txt")) 
dput(clean_options, file.path(thedir, "data_clean_options.txt"))

rjags::load.module("glm")

fitted_nplcm_noreg <- nplcm(data_nplcm_noreg,model_options_no_reg,mcmc_options_no_reg)
image(get_individual_prediction(fitted_nplcm_noreg))

Description

get index of latent status

Usage

get_latent_seq(cause_list, ord, select_latent = NULL, exact = TRUE)

Arguments

Value

a vector of indices

Description

get marginal TPR and FPR for nested model

Usage

get_marginal_rates_nested(slice, res_nplcm, model_options, data_nplcm)

Arguments

Value

a matrix of no. of rows equal to retained MCMC samples, no. of columns equal to the no. of measurement dimensions within a slice.

Description

get marginal TPR and FPR for no nested model

Usage

get_marginal_rates_no_nested(slice, res_nplcm, model_options, data_nplcm)

Arguments

Value

a matrix of no. of rows equal to retained MCMC samples, no. of columns equal to the no. of measurement dimensions within a slice.

Description

The result is equivalent to Euclidean-type calculation after the compositional vector (e.g., etiologic fraction) is centered-log-ratio (CLRB) transformed. For simulation only.

Usage

get_metric(DIR_NPLCM, truth)

Arguments

Value

a vector of (Integrated Squared Aitchison Distance (ISAD), bias-squared, variance, truth)

Description

get etiology samples by names (no regression)

Usage

get_pEti_samp(res_nplcm, model_options)

Arguments

Value

A list:

pEti_mat: a matrix of posterior samples (iteration by cause); overall etiology latent_nm: a vector of character strings representing the names of the causes

Description

get the plotting positions (numeric) for the fitted means; 3 positions for each cell

Usage

get_plot_num(e, height)

Arguments

Value

a triple with numerical plotting positions

Description

get a list of measurement index where to look for data

Usage

get_plot_pos(template)

Arguments

Value

a list of index vectors

Description

Obtain posterior standard deviation from a result folder

Usage

get_postsd(DIR_NPLCM)

Arguments

Value

a vector of positive numbers

Description

get top patterns from a slice of bronze-standard measurement

Usage

get_top_pattern(BrS_dat, Y, case_status, n_pat, exclude_missing = TRUE)

Arguments

Value

a list of results: obs_pat - observed rates; pattern_names; exist_other - if actual no. of patterns is larger than n_pat; N- No. of individuals with Y = case_status.

See Also

Other exploratory data analysis functions: plot_logORmat(), show_individual(), summarize_BrS(), summarize_SS(), visualize_season()

Examples

data(data_nplcm_noreg)
get_top_pattern(data_nplcm_noreg$Mobs$MBS[[1]],data_nplcm_noreg$Y,1,5,FALSE)

data(data_nplcm_noreg)
get_top_pattern(data_nplcm_noreg$Mobs$MBS$MBS1,data_nplcm_noreg$Y,case_status=1,n_pat=5)

Description

Shannon entropy for multivariate discrete data

Usage

H(px)

Arguments

Value

a non-negative number

Examples

H(c(0.5,0.3,0.2))

test if a formula has terms not created by [s_date_Eti() or s_date_FPR()

Description

test if a formula has terms not created by [s_date_Eti() or s_date_FPR()

Usage

has_non_basis(form)

Arguments

Value

logical TRUE (if having terms not created by [s_date_Eti() or s_date_FPR()); FALSE otherwise.

Examples

form1 <- as.formula(~ -1+s_date_FPR(DATE,Y,basis = "ps",10) + as.factor(SITE))
form2 <- as.formula(~ -1+s_date_FPR(DATE,Y,basis = "ps",10))
form3 <- as.formula(~ s_date_FPR(DATE,Y,basis = "ps",10))

has_non_basis(form1)
has_non_basis(form2)
has_non_basis(form3)

Description

Reverse to symb2I()

Usage

I2symb(binary_code, pathogen_list)

Arguments

Value

The name of pathogen or pathogen combination indicated by "code"

Examples

I2symb("001",c("A","B","C"))
I2symb("000",c("A","B","C"))

Description

Convert a matrix of binary indicators to categorical variables

Usage

Imat2cat(binary_mat, cause_list, pathogen_list)

Arguments

Value

A vector of categorical variables. Its length equals the length of "allowed.list"

Examples

Imat2cat(rbind(diag(3),c(1,1,0),c(0,0,0)),c("A","B","C","A+B","NoA"),c("A","B","C"))

Description

Initialize individual latent status (for JAGS)

Usage

init_latent_jags_multipleSS(
  MSS_list,
  cause_list,
  patho = unlist(lapply(MSS_list, colnames))
)

Arguments

Details

In JAGS 3.4.0, if an initial value contradicts the probabilistic specification, e.g. MSS_1[i,j] ~ dbern(mu_ss_1[i,j]), where MSS_1[i,j]=1 but mu_ss_1[i,j]=0, then JAGS cannot understand it. In PERCH application, this is most likely used when the specificity of the silver-standard data is 1. Note: this is not a problem in WinBUGS.

Value

a list of numbers, indicating categories of individual latent causes.

Description

insert distribution for latent status code chunk into .bug file

Usage

insert_bugfile_chunk_noreg_etiology(ppd = NULL)

Arguments

Value

a long character string to be inserted into .bug model file as distribution specification for latent status

Description

Insert measurement likelihood (without regression) code chunks into .bug model file

Usage

insert_bugfile_chunk_noreg_meas(
  k_subclass,
  Mobs,
  prior,
  cause_list,
  use_measurements = "BrS",
  ppd = NULL,
  use_jags = FALSE
)

Arguments

Value

a long character string to be inserted into .bug model file as measurement likelihood

See Also

It is used in write_model_NoReg for constructing a .bug file along with specification of latent status distribution (insert_bugfile_chunk_noreg_etiology)

Description

insert etiology regression for latent status code chunk into .bug file; discrete predictors

Usage

insert_bugfile_chunk_reg_discrete_predictor_etiology(Jcause, ppd = NULL)

Arguments

Value

a long character string to be inserted into .bug model file as distribution specification for latent status

Description

Insert measurement likelihood (with regression; discrete) code chunks into .bug model file

Usage

insert_bugfile_chunk_reg_discrete_predictor_nonest_meas(
  Mobs,
  prior,
  cause_list,
  use_measurements = "BrS",
  ppd = NULL,
  use_jags = FALSE
)

Arguments

Value

A long character string to be inserted into .bug model file as measurement likelihood

See Also

It is used in write_model_Reg_NoNest for constructing a .bug file along with specification of latent status regression (insert_bugfile_chunk_reg_etiology)

Description

insert etiology regression for latent status code chunk into .bug file

Usage

insert_bugfile_chunk_reg_etiology(Eti_formula, Jcause, ppd = NULL)

Arguments

Value

a long character string to be inserted into .bug model file as distribution specification for latent status

Description

Insert measurement likelihood (nested model+regression) code chunks into .bug model file

Usage

insert_bugfile_chunk_reg_nest_meas(
  Mobs,
  prior,
  cause_list,
  FPR_formula,
  use_measurements = "BrS",
  ppd = NULL,
  use_jags = FALSE
)

Arguments

Value

A long character string to be inserted into .bug model file as measurement likelihood

See Also

Called by write_model_Reg_NoNest for constructing a .bug file. This is usually called along with specification of latent status regression (insert_bugfile_chunk_reg_etiology).

Description

Insert measurement likelihood (with regression) code chunks into .bug model file

Usage

insert_bugfile_chunk_reg_nonest_meas(
  Mobs,
  prior,
  cause_list,
  FPR_formula,
  use_measurements = "BrS",
  ppd = NULL,
  use_jags = FALSE
)

Arguments

Value

A long character string to be inserted into .bug model file as measurement likelihood

See Also

It is used in write_model_Reg_NoNest for constructing a .bug file along with specification of latent status regression (insert_bugfile_chunk_reg_etiology)

Description

is_discrete checks if the specified covariates could be regarded as discrete variables.

Usage

is_discrete(X, X_reg)

Arguments

Details

Note that this function should be used with caution. It used

$nrow(X)/nrow(unique(X[,X_reg,drop=FALSE]))>10$

as an ad hoc criterion. It is not the same as is.discrete() in plyr

Value

TRUE for all being discrete; FALSE otherwise.

Description

outputs logical values for a formula; to identify intercept-only formula.

Usage

is_intercept_only(form)

Arguments

Value

TRUE for intercept-only; FALSE otherwise

Description

See if a result folder is obtained by JAGS

Usage

is_jags_folder(DIR_NPLCM)

Arguments

Value

TRUE for from JAGS; FALSE otherwise.

Examples

is_jags_folder(tempdir()) # just an illustration.

Description

check if a list has elements all of length one

Usage

is_length_all_one(x)

Arguments

Value

TRUE or FALSE

Examples

l = list(a = 5, b = 1:2)
is_length_all_one(l) # FALSE
l = list(a = 5, b = 1)
is_length_all_one(l) # TRUE

Description

Test for 'try-error' class

Usage

is.error(x)

Arguments

Value

Logical. TRUE for "try-error"; FALSE otherwise

References

http://adv-r.had.co.nz/Exceptions-Debugging.html

Description

The jags function takes data and starting values as input. It automatically writes a jags script, calls the model, and saves the simulations for easy access in R. Check the R2jags::jags2 for details about the argument.

Usage

jags2_baker(
  data,
  inits,
  parameters.to.save,
  model.file = "model.bug",
  n.chains = 3,
  n.iter = 2000,
  n.burnin = floor(n.iter/2),
  n.thin = max(1, floor((n.iter - n.burnin)/1000)),
  DIC = TRUE,
  jags.path = "",
  working.directory = NULL,
  clearWD = TRUE,
  refresh = n.iter/50
)

Arguments

Details

This modifies the jags2 function in R2jags package.

Value

Same as R2jags::jags()

See Also

R2jags::jags()

Description

Here's a version that works with log-scale and linear scale axes. The trick is to express line locations in npc coordinates rather than user coordinates, since the latter are of course not linear when axes are on log scales.

Usage

line2user(line, side)

Arguments

Details

par('cin')[2] * par('cex') * par('lheight') returns the current line height in inches, which we convert to user coordinates by multiplying by diff(grconvertX(0:1, 'inches', 'user')), the length of an inch in user coordinates (horizontally, in this case - if interested in the vertical height of a line in user coords we would use ⁠diff(grconvertY(0:1, 'inches', 'user')))⁠.

Value

a numeric vector of the same length as line; the values represent the coordinates in the current plot and are converted from line.

References

https://stackoverflow.com/questions/29125019/get-margin-line-locations-mgp-in-user-coordinates

Examples

setup_plot <- function(log = "") {
  oldpar <- par(mar = c(2, 10, 2, 2), oma = rep(2, 4))
  plot.new()
  plot.window(xlim = c(1, 10), ylim = c(1, 10), log = log)
  box(which = "plot", lwd = 2, col = "gray40")
  box(which = "figure", lwd = 2, col = "darkred")
  box(which = "outer", lwd = 2, col = "darkgreen")
  text(x = 0.5, y = 0.5, 
       labels = "Plot Region", 
       col = "gray40", font = 2)
  mtext(side = 3, text = "Figure region", line = 0.5, col = "darkred", font = 2)
  mtext(side = 3, text = "Device region", line = 2.5, col = "darkgreen", font = 2)
  for (i in 0:9) {
    mtext(side = 2, col = "darkred", text = paste0("Line", i), line = i)
  }
  par(oldpar)
}
# And here are a couple of examples, applied to your setup_plot with mar=c(5, 5, 5, 5):
setup_plot()
axis(1, line=5)
axis(2, line=5)
abline(h=line2user(0:4, 1), lty=3, xpd=TRUE)
abline(v=line2user(0:4, 2), lty=3, xpd=TRUE)
abline(h=line2user(0:4, 3), lty=3, xpd=TRUE)
abline(v=line2user(0:4, 4), lty=3, xpd=TRUE)

setup_plot(log='x')
axis(1, line=5)
axis(2, line=5)
abline(h=line2user(0:4, 1), lty=3, xpd=TRUE)
abline(v=line2user(0:4, 2), lty=3, xpd=TRUE)
abline(h=line2user(0:4, 3), lty=3, xpd=TRUE)
abline(v=line2user(0:4, 4), lty=3, xpd=TRUE)


setup_plot(log='y')
axis(1, line=5)
axis(2, line=5)
abline(h=line2user(0:4, 1), lty=3, xpd=TRUE)
abline(v=line2user(0:4, 2), lty=3, xpd=TRUE)
abline(h=line2user(0:4, 3), lty=3, xpd=TRUE)
abline(v=line2user(0:4, 4), lty=3, xpd=TRUE)

setup_plot(log='xy')
axis(1, line=5)
axis(2, line=5)
abline(h=line2user(0:4, 1), lty=3, xpd=TRUE)
abline(v=line2user(0:4, 2), lty=3, xpd=TRUE)
abline(h=line2user(0:4, 3), lty=3, xpd=TRUE)
abline(v=line2user(0:4, 4), lty=3, xpd=TRUE)